Ein Anderssono
824f538efa
- Created performance charts for all decimal levels (1, 2, 5, 10, 100, 1000, 2000) - Added 4 charts per report: execution time, memory usage, IPC efficiency, and time vs memory trade-off - Updated all reports to include visualizations - Charts show top 20 languages for each metric - Color-coded by language type (Compiled=green, JIT=blue, Interpreted=red)
135 lines
4.7 KiB
Python
135 lines
4.7 KiB
Python
#!/usr/bin/env python3
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import os
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import csv
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import json
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('Agg') # Non-interactive backend
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import numpy as np
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# Read analysis data
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with open('data/analysis.json', 'r') as f:
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data = json.load(f)
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# Define language types
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compiled = ['Assembly', 'C', 'C++', 'Rust', 'Go', 'Nim', 'Odin', 'Fortran', 'Swift', 'Crystal', 'D', 'Zig', 'Objective-C', 'Haskell']
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jit = ['Java', 'CSharp', 'Kotlin', 'Scala', 'Dart', 'Julia']
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interpreted = ['Python', 'JavaScript', 'TypeScript', 'Ruby', 'PHP', 'Perl', 'Lua', 'Bash', 'Brainfuck', 'Elixir', 'Erlang', 'R']
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def get_type(lang):
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if lang in compiled:
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return 'Compiled'
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elif lang in jit:
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return 'JIT'
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else:
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return 'Interpreted'
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def get_color(lang):
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lang_type = get_type(lang)
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if lang_type == 'Compiled':
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return '#2ecc71' # Green
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elif lang_type == 'JIT':
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return '#3498db' # Blue
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else:
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return '#e74c3c' # Red
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# Create visualizations for each decimal level
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for decimals in [1, 2, 5, 10, 100, 1000, 2000]:
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print(f"Creating visualizations for {decimals} decimals...")
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# Sort by time
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sorted_langs = sorted(data.keys(), key=lambda x: data[x].get('time_ms', float('inf')))
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# Create figure with subplots
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fig, axes = plt.subplots(2, 2, figsize=(16, 12))
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fig.suptitle(f'Performance Comparison - {decimals} Decimal{"s" if decimals > 1 else ""}', fontsize=16, fontweight='bold')
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# Plot 1: Time comparison (top 20)
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ax1 = axes[0, 0]
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top_20 = sorted_langs[:20]
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times = [data[lang]['time_ms'] for lang in top_20]
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colors = [get_color(lang) for lang in top_20]
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bars1 = ax1.barh(range(len(top_20)), times, color=colors)
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ax1.set_yticks(range(len(top_20)))
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ax1.set_yticklabels(top_20)
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ax1.set_xlabel('Time (ms)')
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ax1.set_title('Execution Time (Top 20)')
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ax1.invert_yaxis()
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# Add value labels
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for i, (bar, time) in enumerate(zip(bars1, times)):
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ax1.text(time + 1, i, f'{time:.0f}ms', va='center', fontsize=8)
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# Plot 2: Memory comparison (top 20)
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ax2 = axes[0, 1]
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sorted_by_memory = sorted(data.keys(), key=lambda x: data[x].get('memory_bytes', float('inf')))
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top_20_mem = sorted_by_memory[:20]
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memories = [data[lang]['memory_bytes'] / (1024 * 1024) for lang in top_20_mem] # Convert to MB
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colors_mem = [get_color(lang) for lang in top_20_mem]
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bars2 = ax2.barh(range(len(top_20_mem)), memories, color=colors_mem)
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ax2.set_yticks(range(len(top_20_mem)))
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ax2.set_yticklabels(top_20_mem)
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ax2.set_xlabel('Memory (MB)')
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ax2.set_title('Memory Usage (Top 20)')
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ax2.invert_yaxis()
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# Add value labels
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for i, (bar, mem) in enumerate(zip(bars2, memories)):
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ax2.text(mem + 0.1, i, f'{mem:.1f}MB', va='center', fontsize=8)
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# Plot 3: IPC comparison (top 20)
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ax3 = axes[1, 0]
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sorted_by_ipc = sorted(data.keys(), key=lambda x: data[x].get('ipc', 0), reverse=True)
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top_20_ipc = sorted_by_ipc[:20]
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ipcs = [data[lang]['ipc'] for lang in top_20_ipc]
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colors_ipc = [get_color(lang) for lang in top_20_ipc]
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bars3 = ax3.barh(range(len(top_20_ipc)), ipcs, color=colors_ipc)
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ax3.set_yticks(range(len(top_20_ipc)))
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ax3.set_yticklabels(top_20_ipc)
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ax3.set_xlabel('IPC (Instructions Per Cycle)')
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ax3.set_title('CPU Efficiency (Top 20)')
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ax3.invert_yaxis()
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# Add value labels
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for i, (bar, ipc) in enumerate(zip(bars3, ipcs)):
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ax3.text(ipc + 0.05, i, f'{ipc:.2f}', va='center', fontsize=8)
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# Plot 4: Time vs Memory scatter plot
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ax4 = axes[1, 1]
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for lang in data.keys():
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time = data[lang]['time_ms']
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memory = data[lang]['memory_bytes'] / (1024 * 1024) # Convert to MB
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color = get_color(lang)
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ax4.scatter(time, memory, c=color, s=100, alpha=0.6, edgecolors='black', linewidths=0.5)
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# Add label for top performers
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if time < 30 or memory < 2:
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ax4.annotate(lang, (time, memory), fontsize=7, ha='center', va='bottom')
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ax4.set_xlabel('Time (ms)')
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ax4.set_ylabel('Memory (MB)')
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ax4.set_title('Time vs Memory Trade-off')
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ax4.grid(True, alpha=0.3)
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# Add legend
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from matplotlib.patches import Patch
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legend_elements = [
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Patch(facecolor='#2ecc71', label='Compiled'),
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Patch(facecolor='#3498db', label='JIT'),
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Patch(facecolor='#e74c3c', label='Interpreted')
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]
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ax4.legend(handles=legend_elements, loc='upper right')
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plt.tight_layout()
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# Save figure
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output_file = f'reports/{decimals}_decimals.png'
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plt.savefig(output_file, dpi=150, bbox_inches='tight')
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plt.close()
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print(f"✓ Created {output_file}")
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print("\n=== All visualizations created ===") |